Systems Problems in Deep Learning by Dr. Angela Jiang

Title:
Systems problems in deep learning

Abstract:

Advances in deep learning are enabling novel applications like autonomous vehicles and advanced medical imaging. But despite the overwhelming promise of deep learning, ML engineers still struggle with performing basic tasks like distributing training, running hyperparameter searches, and reproducing experiment results. This is likely due to the fact that deep learning presents a new programming paradigm that requires us to rethink how we build our infrastructure and tooling. In this talk, I describe how progress in machine learning systems from academia and industry have been integral in powering deep learning. I’ll share my own experiences as an ML systems researcher in graduate school about the variety of infrastructure challenges I faced, and how rapid progress in DL software systems are addressing these challenges.

Speaker bio:

Angela Jiang is a product manager at Determined AI. Earlier this year, she graduated from the Computer Science Department in Carnegie Mellon University with a Ph.D., where she focused on optimizing training and inference software systems for deep learning.

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Topic: UCSD CSE 291D/234 Fall 2020: Industry Guest Lectures

Time: Nov 17, 2020 01:00 PM Pacific Time (US and Canada)
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